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SkillResolve-Bench: Measuring and Resolving Same-Capability Ambiguity in Agent Skill Retrieval

T0 review · 1 major / 0 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read Agent skill retrieval can eliminate exposure to risky same-capability variants by selecting family representatives.

desk verdict SkillResolve-Bench and the family-resolution method target same-capability ambiguity in agent skill retrieval with measurable gains on harmful sibling rate. read the letter →

arxiv 2606.10388 v1 pith:3LT6FBMA submitted 2026-06-09 cs.IR cs.AI

Jiandong Ding This is my paper · ORCID
classification cs.IRcs.AI
keywords skillretrievalagentskillssame-capabilityambiguityharmfulsiblingraterepresentativeselectionbenchmarkinformationexecutionrisk
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper shows that retrieving the right capability family is not enough for agent skill libraries because the wrong representative can introduce execution risks such as stale resources or incorrect procedures. SkillResolve-Bench provides 661 helpful/risky pairs along with family relations to evaluate both ranking quality and the rate of harmful sibling exposure. The SkillResolve method identifies families, scores utility using confusable negatives and cues, then picks one representative per family. This yields Recall@3 of 0.766 and NDCG@3 of 0.699 with zero harmful sibling rate at K=3, outperforming prior methods.

What carries the argument

Within-family representative selection after resolving capability families, which prevents harmful sibling exposure in top-K results.

What would settle it

Running the benchmark on an independent collection of agent skills and checking if the HSR@3 remains zero for the SkillResolve method while maintaining the reported recall levels.

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Extended reading notes

Core claim

Each query in the benchmark pairs a helpful skill with a query-specific risky sibling that shares the capability family but can lead to execution problems. SkillResolve resolves active candidate families, scores query-conditioned utility from confusable library negatives and contract-profile cues, and selects one representative from each family before the final top-K list. Under the released family relation, SkillResolve reaches Recall@3 0.766 and NDCG@3 0.699 while keeping HSR@3=0. It improves over SkillRouter by 0.112 Recall@3 and 0.165 NDCG@3 while reducing HSR@3 from 0.693 to 0. Without representative selection, HSR@3 rises to 0.236 under the same scorer.

Load-bearing premise

The 661 helpful/risky pairs and the family relations supplied with the benchmark correctly capture genuine same-capability execution-risk distinctions that occur in real agent skill libraries, without artificial construction artifacts or unrepresentative sampling.

Editorial extensions

If this is right

  • The representative selection mechanism reduces HSR@3 to 0 while preserving high Recall@3 and NDCG@3.
  • Omitting representative selection increases HSR@3 to 0.236 with the same scoring function.
  • SkillResolve improves Recall@3 by 0.112 and NDCG@3 by 0.165 compared to SkillRouter.
  • The benchmark supports auditing through source-role evidence, cue/leakage checks, and query-disjoint splits.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Libraries could adopt family annotations to support safer skill composition in agents.
  • The approach may extend to other domains like code retrieval where similar implementations carry different risks.
  • Real-world agent runs on benchmark tasks could test if lower HSR correlates with fewer failures.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The paper claims that skill retrieval for agent libraries can surface the wrong same-capability representative, exposing execution risks even when the capability family is correct; it introduces SkillResolve-Bench (661 helpful/risky pairs, 7,982-candidate pool, query-disjoint splits, cue/leakage checks) and the SkillResolve method (family resolution + query-conditioned scoring + representative selection) that reports Recall@3=0.766, NDCG@3=0.699, HSR@3=0, outperforming SkillRouter by 0.112/0.165 while driving HSR@3 from 0.693 to 0, with an ablation showing HSR@3 rises to 0.236 without representative selection.

Significance. If the benchmark pairs and family relations validly reflect real execution-risk distinctions, the work identifies a practically important failure mode in agent skill retrieval and supplies both a measurable benchmark and a concrete mitigation (within-family representative selection) whose contribution is isolated by ablation; the released benchmark and explicit ablation isolating the representative-selection step are strengths that support reproducibility and mechanistic insight.

major comments (1)
  1. [Abstract] Abstract (benchmark construction paragraph): the headline metrics (Recall@3 0.766, NDCG@3 0.699, HSR@3=0) and the claim that representative selection turns capability retrieval into safer procedural exposure presuppose that the 661 helpful/risky pairs and released family relations capture genuine same-capability execution-risk distinctions arising in real libraries; the text lists source-role evidence, cue/leakage checks, and query-disjoint splits but supplies no inter-annotator agreement figures, quantitative bias audit, or sampling audit, leaving the zero-HSR result and the ablation unsupported for transfer.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for acknowledging the practical importance of same-capability ambiguity as well as the strengths of the released benchmark and ablation. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (benchmark construction paragraph): the headline metrics (Recall@3 0.766, NDCG@3 0.699, HSR@3=0) and the claim that representative selection turns capability retrieval into safer procedural exposure presuppose that the 661 helpful/risky pairs and released family relations capture genuine same-capability execution-risk distinctions arising in real libraries; the text lists source-role evidence, cue/leakage checks, and query-disjoint splits but supplies no inter-annotator agreement figures, quantitative bias audit, or sampling audit, leaving the zero-HSR result and the ablation unsupported for transfer.

    Authors: We agree that the manuscript would benefit from expanded quantitative validation of the benchmark construction. The 661 pairs were produced via deterministic source-role extraction from the original SkillRet library metadata together with the 6,660 public candidates; risk labels follow three explicitly defined execution-risk categories (stale resource, missing precondition, wrong procedure). Cue/leakage checks and query-disjoint splits were applied to eliminate trivial or contaminated queries. Because the labeling process was rule-based rather than free-form subjective annotation, inter-annotator agreement was not computed. In the revised version we will add a dedicated “Benchmark Construction Validation” subsection that reports: (i) a sampling audit giving the distribution of risk types and family sizes, (ii) a quantitative bias audit comparing family statistics in the 661-pair set against the full 7,982-candidate pool, and (iii) explicit discussion of how the source-role evidence supports transfer to other libraries. These additions will strengthen the support for the reported HSR@3=0 result and the ablation isolating representative selection. The ablation itself remains internally valid because it holds the scorer fixed and varies only the representative-selection step. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; empirical evaluation on newly introduced benchmark

full rationale

The paper introduces SkillResolve-Bench with 661 helpful/risky pairs and released family relations, then reports direct empirical metrics (Recall@3 0.766, NDCG@3 0.699, HSR@3=0) for SkillResolve versus baselines like SkillRouter on query-disjoint splits. No equations, fitted parameters, or self-citations appear in the provided text. The representative selection step uses the benchmark's supplied family relations to produce HSR@3=0, but this is an explicit design choice evaluated against fixed ground-truth pairs rather than a redefinition or statistical forcing of the reported gains. The ablation (HSR@3 rising to 0.236 without selection) is likewise a direct measurement. This matches the default case of a self-contained empirical study with no load-bearing reductions to inputs by construction.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central claim rests on the validity of the defined skill families and the 661 pairs as representative of real execution risks; no free parameters or new entities are introduced in the abstract.

assumptions (2)
  • domain assumption Skills sharing a capability family can be distinguished by execution-risk attributes such as resource staleness or precondition mismatch.
    Invoked to define helpful/risky pairs and the resolution step.
  • domain assumption The benchmark construction with source-role evidence, cue/leakage checks, and query-disjoint splits produces unbiased test cases.
    Required for the reported Recall, NDCG, and HSR numbers to be meaningful.

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Cite this review

Pith. "Pith review of SkillResolve-Bench: Measuring and Resolving Same-Capability Ambiguity in Agent Skill Retrieval." pith.science (2026). https://pith.science/paper/3LT6FBMA

@misc{pith2026260610388,
  author       = {Pith},
  title        = {Pith review of: SkillResolve-Bench: Measuring and Resolving Same-Capability Ambiguity in Agent Skill Retrieval},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3LT6FBMA}},
  note         = {Machine review of arXiv:2606.10388}
}
read the original abstract

Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution assumptions to an agent. This makes skill retrieval more than broad relevance matching. A retriever can find the right capability family yet expose the wrong same-capability representative. We study this failure as same-capability execution-risk retrieval. Each query pairs a helpful skill with a query-specific risky sibling that shares the capability family but can lead execution toward a stale resource, missing precondition, or wrong procedure. We introduce SkillResolve-Bench 1.0, an auditable benchmark for this setting with 661 helpful/risky pairs, source-role and admission evidence, cue/leakage checks, query-disjoint splits, and a 7,982-candidate pool that includes 6,660 public SkillRet candidates. The benchmark reports helpful ranking together with harmful sibling rate (HSR@K), the top-K exposure of the risky sibling. We also provide SkillResolve, a reference method that resolves active candidate families, scores query-conditioned utility from confusable library negatives and contract-profile cues, and selects one representative from each family before the final top-K list. Under the released family relation, SkillResolve reaches Recall@3 0.766 and NDCG@3 0.699 while keeping HSR@3=0. It improves over SkillRouter by 0.112 Recall@3 and 0.165 NDCG@3 while reducing HSR@3 from 0.693 to 0. Without representative selection, HSR@3 rises to 0.236 under the same scorer, identifying within-family representative choice as the mechanism that turns capability retrieval into safer procedural exposure.

Figures

Figures reproduced from arXiv: 2606.10388 by the authors.

Figure 1
Figure 1. Same-capability execution-risk retrieval. As skill libraries grow, a query may retrieve close siblings [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. SkillResolve-Bench 1.0 construction pipeline. Label admission, public library pressure, integrity [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents

    cs.AI 2026-08 conditional novelty 5.0 of 10

    A lightweight post-retrieval gate trained on verifier feedback can skip most low-value LLM agent executions while retaining most of the achievable reward.

Reference graph

Works this paper leans on

38 extracted references · cited by 1 Pith paper

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Reviewed June 27, 2026 · model on record in the stance chip above.